Biodepth

Dados

Biomassa

BIOMASSA <- read.csv("biodepth/BiodepthSpeciesBiomass.csv", sep = ";", h=T)

Sweden e Sheffield não tem funções de solo medidas & só tem funcoes medidas pra o ano 3 + portugal só sobra 2 amostras quando tira as especies que nao tenho traits = vou filtrar esses

BIOMASSA <- BIOMASSA %>%
  filter(!(site %in% c("Sweden", "Sheffield", "Portugal"))) %>%
  filter(year == 3)

###Biomassa relativa

BIOMASSA <- BIOMASSA %>%
  group_by(year, site, block, plot) %>%
  mutate(percentage_biomass = 100 * biomass / sum(biomass, na.rm = TRUE))

datatable(BIOMASSA, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

ggplot(BIOMASSA, aes(x = biomass)) +
  geom_histogram(binwidth = 10, fill = "lightblue", color = "black") +
  labs(title = "Biomassa Biodepth", 
       x = "Biomassa", 
       y = "Frequência") +
  theme_minimal()

ggplot(BIOMASSA, aes(x = percentage_biomass)) +
  geom_histogram(binwidth = 10, fill = "lightblue", color = "black") +
  labs(title = "Biomassa relativa Biodepth", 
       x = "Biomassa relativa", 
       y = "Frequência") +
  theme_minimal()

Passando de biomassa para composição de espécies

COMPOSICAO <- BIOMASSA %>% 
  group_by(year,location,site,block,plot,sr,fr,fgc,mix.nest,mix,grass,forb,legume,funct,GRASS,FORB,LEG) %>% 
  pivot_wider(
    names_from = species,
    values_from = percentage_biomass,
    values_fill = list(percentage_biomass=0))

#matrix de abundancia
abundance <- COMPOSICAO %>%
  ungroup() %>%
  mutate(unique_plot = paste(plot, block, year, site, sep = "_")) %>%
  group_by(unique_plot) %>%
  summarise(across(where(is.numeric), sum, na.rm = TRUE)) %>%
  column_to_rownames(var = "unique_plot") %>% 
  dplyr::select(-year,-location, -sr, -fr,-fgc, -mix.nest, -mix, -biomass)

# Cria a coluna 'riqueza' contando o número de espécies em cada plot
abundance$riqueza <- rowSums(abundance > 0)
abundance_filtrado <- abundance[abundance$riqueza > 3, ]
abundance_filtrado$riqueza <- NULL

#Remover espécies que não aparecem em nenhum plot
abundance_filtrado <- abundance_filtrado[, colSums(abundance_filtrado, na.rm = TRUE) > 0]

abundance_matrix <- as.matrix(abundance_filtrado)

FUNCIONAL

fd <- dbFD(trait_dat, abundance_matrix)

## FRic: Dimensionality reduction was required. The last 51 PCoA axes (out of 54 in total) were removed. 
## FRic: Quality of the reduced-space representation = 0.970528

Functional <- data.frame(raoq = fd$RaoQ, FRic = fd$FRic,FEve = fd$FEve, FDis = fd$FDis, FD = fd$nbsp )

datatable(Functional, options= list(deferRender = TRUE,   scroller = TRUE,  scrollX=T, scrollY = "350px", autoWidth=T), rownames=T)

hist(Functional$FRic)

hist(Functional$raoq)

MULTIFUNCIONALIDADE

multi <- cbind(dataset_biodep1, getStdAndMeanFunctions(dataset_biodep1, vars))

Dataset final

dataset_final <- multi %>%
  left_join(Functional, by = c("plot", "site"))

datatable(dataset_final, options= list(deferRender = TRUE,    scroller = TRUE, scrollY = "350px", scrollX=T, autoWidth=T), rownames=T)

Análise

cor_matrix

##            raoq        FRic       FEve      FDis         FD
## raoq 1.00000000  0.08562603  0.3526263 0.9650922  0.1035145
## FRic 0.08562603  1.00000000 -0.2093003 0.1011771  0.8329241
## FEve 0.35262633 -0.20930027  1.0000000 0.4045607 -0.1835774
## FDis 0.96509217  0.10117705  0.4045607 1.0000000  0.1378217
## FD   0.10351450  0.83292415 -0.1835774 0.1378217  1.0000000

ggcorrplot(cor_matrix, 
           method = "circle", 
           type = "lower", 
           lab = TRUE, 
           title = "Índices de Diversidade Funcional")

Mixed model

mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|site),data = biodepth[-46,])
summary(mod1)

##  Family: gaussian  ( identity )
## Formula:          meanFunction ~ raoq + FRic + (1 | site)
## Data: biodepth[-46, ]
## 
##      AIC      BIC   logLik deviance df.resid 
##   -216.1   -202.4    113.0   -226.1      108 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance Std.Dev.
##  site     (Intercept) 0.005686 0.07541 
##  Residual             0.006957 0.08341 
## Number of obs: 113, groups:  site, 5
## 
## Dispersion estimate for gaussian family (sigma^2): 0.00696 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  3.656e-01  3.749e-02   9.752  < 2e-16 ***
## raoq        -9.716e-04  5.443e-04  -1.785   0.0742 .  
## FRic         3.082e-04  7.098e-05   4.341 1.42e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod_resid <- simulateResiduals(fittedModel = mod1, plot = FALSE)
plot(mod_resid) # resíduos não tá normal

outliers(mod_resid)

## [1] 40

ggplot(biodepth, aes(x = raoq, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

ggplot(biodepth, aes(x = raoq, y = meanFunction, color = as.factor(site))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(site)), method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

Ceedar Creek

e097

Avaliar o efeito de diferentes níveis de fertilização com nitrogênio em um experimento de competição de árvores.

9 TRTAMENTOS de adição de nitrogênio:
1: 0g/m²/ano nitrogênio (com micronutrientes)
2: 3g/m²/ano nitrogênio (com micronutrientes)
3: 6g/m²/ano nitrogênio (com micronutrientes)
4: 10g/m²/ano nitrogênio (com micronutrientes)
5: 16g/m²/ano nitrogênio (com micronutrientes)
6: 28g/m²/ano nitrogênio (com micronutrientes)
7: 50g/m²/ano nitrogênio (com micronutrientes)
8: 80g/m²/ano nitrogênio (com micronutrientes)
9: 0g/m²/ano (sem nitrogênio sem micronitrientes)

Dados

Biomassa

biomassa <- read.table("e097/e097_Plant aboveground biomass data.txt", h=T, sep="\t")

Filtrando o ano 1982 porque as funções foram coletadas aqui.

biomassa <- biomassa %>%  
  filter(year=="1982")

Biomassa relativa

biomassa <- biomassa %>%
  group_by(Field, plot.number) %>%
  mutate(percentage_biomass = 100 * Species.Biomass..g.m2. / sum(Species.Biomass..g.m2., na.rm = TRUE))

datatable(biomassa, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

ggplot(biomassa, aes(x = percentage_biomass)) +
  geom_histogram(binwidth = 10, fill = "lightblue", color = "black") +
  labs(title = "Biomassa relativa CEDAR CREEK e97", 
       x = "Biomassa relativa", 
       y = "Frequência") +
  theme_minimal()

###FUNCIONAL

# Cálculo da diversidade funcional
fd <- dbFD(trait_dat, abundance_matrix_clean)

## FRic: Dimensionality reduction was required. The last 60 PCoA axes (out of 64 in total) were removed. 
## FRic: Quality of the reduced-space representation = 0.9789017

Functional <- data.frame(raoq = fd$RaoQ, FRic = fd$FRic,FEve = fd$FEve, FDis = fd$FDis, FD = fd$nbsp )

datatable(Functional, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

MULTIFUNCIONALIDADE

#Dataset junto das funções
funcoes <- potassioFunc %>%
  full_join(fosforoFunc, by = c("Plot.number", "Field.letter")) %>%
  full_join(calcioFunc, by = c("Plot.number", "Field.letter")) %>%
  full_join(magnesioFunc, by = c("Plot.number", "Field.letter")) %>%
  full_join(nitroFunc, by = c("Plot.number", "Field.letter")) %>%
  full_join(phFunc, by = c("Plot.number", "Field.letter"))

e97_multifuncionalidade <- cbind(datset_97, getStdAndMeanFunctions(datset_97, vars))

Dataset final

dataset_final_e97 <- e97_multifuncionalidade %>%
  left_join(Functional, by = c("Plot.number", "treatment", "Field.letter"))

datatable(dataset_final_e97, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

Análise

cor_matrix

##            raoq      FRic       FEve       FDis          FD
## raoq  1.0000000 0.1762663 0.19790931  0.9697383 -0.19715587
## FRic  0.1762663 1.0000000 0.15502712  0.1612947  0.79029689
## FEve  0.1979093 0.1550271 1.00000000  0.3268466  0.04754828
## FDis  0.9697383 0.1612947 0.32684660  1.0000000 -0.18431343
## FD   -0.1971559 0.7902969 0.04754828 -0.1843134  1.00000000

ggcorrplot(cor_matrix, 
           method = "circle", 
           type = "lower", 
           lab = TRUE, 
           title = "Índices de Diversidade Funcional")

Mixed model

mod1 <- glmmTMB(log(meanFunction) ~ raoq + FRic + (1|Field.letter),data = e97)
summary(mod1)

##  Family: gaussian  ( identity )
## Formula:          log(meanFunction) ~ raoq + FRic + (1 | Field.letter)
## Data: e97
## 
##      AIC      BIC   logLik deviance df.resid 
##   -247.4   -234.0    128.7   -257.4      103 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance Std.Dev.
##  Field.letter (Intercept) 0.008875 0.09421 
##  Residual                 0.004963 0.07045 
## Number of obs: 108, groups:  Field.letter, 2
## 
## Dispersion estimate for gaussian family (sigma^2): 0.00496 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.713e-01  8.333e-02  -4.456 8.36e-06 ***
## raoq        -1.744e-03  8.262e-04  -2.111   0.0348 *  
## FRic        -6.280e-06  5.547e-05  -0.113   0.9099    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod_resid <- simulateResiduals(fittedModel = mod1, plot = FALSE)
plot(mod_resid) # resíduos não tá normal

outliers(mod_resid)

## [1] 26

ggplot(e97, aes(x = raoq, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

ggplot(e97, aes(x = raoq, y = meanFunction, color = as.factor(Field.letter))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(Field.letter)), method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

#Tirando outliers
e97_semot <- e97 %>% 
  filter(!raoq <40) %>%   
  filter(!meanFunction >0.9)

mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|Field.letter),data = e97_semot)
summary(mod1)

##  Family: gaussian  ( identity )
## Formula:          meanFunction ~ raoq + FRic + (1 | Field.letter)
## Data: e97_semot
## 
##      AIC      BIC   logLik deviance df.resid 
##   -365.3   -352.0    187.6   -375.3      101 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance Std.Dev.
##  Field.letter (Intercept) 0.002786 0.05278 
##  Residual                 0.001558 0.03947 
## Number of obs: 106, groups:  Field.letter, 2
## 
## Dispersion estimate for gaussian family (sigma^2): 0.00156 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  6.475e-01  4.952e-02  13.076   <2e-16 ***
## raoq        -5.073e-04  5.304e-04  -0.956    0.339    
## FRic         1.815e-05  3.150e-05   0.576    0.564    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(e97_semot, aes(x = raoq, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

ggplot(e97_semot, aes(x = raoq, y = meanFunction, color = as.factor(Field.letter))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(Field.letter)), method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

ggplot(e97_semot, aes(x = FRic, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "FRic",
       x = "FRic", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

e120

Biodiversity II, effects od plant biodiversity on population and ecossystem process

Determinar como a quantidade de espécies de plantas afeta processos ecológicos nos níveis populacional, de comunidade e ecossistêmico. Como a diversidade de plantas influencia a produção de biomassa, a estabilidade do ecossistema, a incidência de doenças, a diversidade de insetos herbívoros e predadores, e outros parâmetros do solo.

168 parcelas de 9m x 9m.

Tratamentos: Parcelas com 1, 2, 4, 8 ou 16 espécies de plantas, com cerca de 30 réplicas para cada nível de diversidade.

COMPOSIÇÃO DAS ESPECIES

datatable(cover, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

# Calcular a cobertura relativa por quadrante dentro de cada plot
cover <- cover %>%
  group_by(Year, Plot, Quadrat) %>%
  mutate(percentage_cover = 100 * Abscover / sum(Abscover, na.rm = TRUE))

# Somar a cobertura por espécie dentro de cada plot e ano
cover <- cover %>%
  group_by(Year, Plot, Species) %>%
  summarize(Total_Abscover = sum(percentage_cover, na.rm = TRUE)) %>%
  ungroup()

## `summarise()` has grouped output by 'Year', 'Plot'. You can override using the
## `.groups` argument.

# Padronizar para que a soma total seja 100% dentro de cada plot
cover <- cover %>%
  group_by(Year, Plot) %>%
  mutate(cover_final = 100 * Total_Abscover / sum(Total_Abscover, na.rm = TRUE)) %>%
  ungroup()

ggplot(cover, aes(x = Total_Abscover)) +
geom_histogram(bins = 50, fill = "blue", color = "black")

ggplot(cover, aes(x = cover_final)) +
  geom_histogram(bins = 50, fill = "blue", color = "black")

FUNCIONAL

fd_120 <- dbFD(trait_dat_120, abundance_120_matrix_clean)

## FEVe: Could not be calculated for communities with <3 functionally singular species. 
## FDis: Equals 0 in communities with only one functionally singular species. 
## FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species. 
## FRic: Dimensionality reduction was required. The last 75 PCoA axes (out of 77 in total) were removed. 
## FRic: Quality of the reduced-space representation = 0.9697847 
## FDiv: Could not be calculated for communities with <3 functionally singular species.

Functional_120 <- data.frame(raoq = fd_120$RaoQ, FRic = fd_120$FRic,FEve = fd_120$FEve, FDis = fd_120$FDis, FD = fd_120$nbsp )

hist(Functional_120$raoq)

hist(Functional_120$FRic)

MULTIFUNCIONALIDADE

#quantidades de amostragem tudo diferente nos anos 
#carbon_nitroFunc 1164
#biomassaFunc 1862
#soil_carbonFunc 726
#nitrat_amoniFunc 1194
#soil_nitroFunc 726

data_list <- list(carbon_nitroFunc, biomassaFunc, soil_carbonFunc, nitrat_amoniFunc, soil_nitroFunc)
combined_data <- reduce(data_list, full_join, by = c("Year", "Plot"))

#remover todos os dados ausentes
#so sobra 1996 e 2000
funcoes_120 <- na.omit(combined_data)

e120_multifuncionalidade <- cbind(dataset_120, getStdAndMeanFunctions(dataset_120, vars120))

Dataset COMPLETO

dataset_final_120 <- e120_multifuncionalidade %>%
  left_join(Functional_120, by = c("Plot", "Year")) 

datatable(dataset_final_120, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

#Vou apenas cortar
##Help####
dataset_final_120 <- dataset_final_120 %>% 
  filter(FDis < 10, raoq < 50, FRic < 200)

Análise

cor_matrix

##           raoq       FRic        FEve      FDis         FD
## raoq 1.0000000  0.2782868  0.22545819 0.9205079 0.28265829
## FRic 0.2782868  1.0000000 -0.13035791 0.3107401 0.63200557
## FEve 0.2254582 -0.1303579  1.00000000 0.1708288 0.06211653
## FDis 0.9205079  0.3107401  0.17082879 1.0000000 0.34517506
## FD   0.2826583  0.6320056  0.06211653 0.3451751 1.00000000

ggcorrplot(cor_matrix, 
           method = "circle", 
           type = "lower", 
           lab = TRUE, 
           title = "Índices de Diversidade Funcional")

Mixed model

mod1 <- glmmTMB(log(meanFunction) ~ raoq + FRic + (1|Year),data = e120)
summary(mod1)

##  Family: gaussian  ( identity )
## Formula:          log(meanFunction) ~ raoq + FRic + (1 | Year)
## Data: e120
## 
##      AIC      BIC   logLik deviance df.resid 
##   -173.4   -154.7     91.7   -183.4      304 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance  Std.Dev.
##  Year     (Intercept) 0.0005291 0.0230  
##  Residual             0.0320813 0.1791  
## Number of obs: 309, groups:  Year, 2
## 
## Dispersion estimate for gaussian family (sigma^2): 0.0321 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.0295926  0.0336842 -30.566  < 2e-16 ***
## raoq         0.0059317  0.0012417   4.777 1.78e-06 ***
## FRic         0.0001530  0.0003451   0.443    0.657    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod_resid <- simulateResiduals(fittedModel = mod1, plot = FALSE)
plot(mod_resid) # resíduos não tá normal

outliers(mod_resid)

## [1]   5  39 166 219 261

ggplot(e120, aes(x = raoq, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(e120, aes(x = raoq, y = meanFunction, color = as.factor(Year))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(Year)), method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 9 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).

#Tirando outliers
e120_semot <- e120 %>% 
  filter(!raoq >100) %>% 
  filter(!FRic >150)

mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|Year),data = e120_semot)
summary(mod1)

##  Family: gaussian  ( identity )
## Formula:          meanFunction ~ raoq + FRic + (1 | Year)
## Data: e120_semot
## 
##      AIC      BIC   logLik deviance df.resid 
##   -733.3   -714.7    371.7   -743.3      300 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance  Std.Dev.
##  Year     (Intercept) 5.007e-05 0.007076
##  Residual             5.088e-03 0.071328
## Number of obs: 305, groups:  Year, 2
## 
## Dispersion estimate for gaussian family (sigma^2): 0.00509 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 0.3620884  0.0133443  27.134  < 2e-16 ***
## raoq        0.0020775  0.0005000   4.155 3.26e-05 ***
## FRic        0.0001019  0.0001495   0.682    0.495    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(e120_semot, aes(x = raoq, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(e120_semot, aes(x = raoq, y = meanFunction, color = as.factor(Year))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(Year)), method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 9 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(e120_semot, aes(x = FRic, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "FRic",
       x = "FRic", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 9 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(e120_semot, aes(x = FRic, y = meanFunction, color=as.factor(Year))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(Year)), method = "lm", se = FALSE) +
  labs(title = "FRic",
       x = "FRic", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 9 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).

Jena dBEF

Tratamento 1 - Sem história de solo e sem história de plantas: O solo e a camada de plantas foram removidos até 30 cm de profundidade e substituídos por solo de um campo arável adjacente. Tratamento 2 - Com história de solo, mas sem história de plantas: A camada de plantas foi removida, mas o solo original foi mantido. Tratamento 3 - Com história de solo e de plantas: As parcelas existentes do experimento principal, estabelecidas desde 2002, foram mantidas como controle de longo prazo, mantendo tanto a história do solo quanto a das plantas. Esses tratamentos foram projetados para testar como a história do solo e das plantas influenciam a produtividade vegetal e as interações ecológicas ao longo do tempo, considerando tanto a diversidade de plantas quanto os efeitos do histórico do solo sobre a biomassa e outros fatores ecológicos.

80 plots 3 tratamentos 3 anos

COMPOSIÇÃO DAS ESPÉCIES: cobertura

cobertura_2017 <- read.csv("dbef/PLANT_COVER_2017.csv", sep = ";")
cobertura_2018 <- read.csv("dbef/PLANT_COVER_2018.csv", sep = ";")
cobertura_2019 <- read.csv("dbef/PLANT_COVER_2019.csv", sep = ";")

cobertura_total <- bind_rows(cobertura_2017, cobertura_2018, cobertura_2019)
datatable(cobertura_total, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

FUNCIONAL

#diversidade funcional
fd_dbef <- dbFD(trait_dat_dbef, abundance_dbef_matrix_clean)

## FEVe: Could not be calculated for communities with <3 functionally singular species. 
## FDis: Equals 0 in communities with only one functionally singular species. 
## FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species. 
## FRic: Dimensionality reduction was required. The last 57 PCoA axes (out of 59 in total) were removed. 
## FRic: Quality of the reduced-space representation = 0.7574923 
## FDiv: Could not be calculated for communities with <3 functionally singular species.

Functional_dbef <- data.frame(raoq = fd_dbef$RaoQ, FRic = fd_dbef$FRic,FEve = fd_dbef$FEve, FDis = fd_dbef$FDis, FD = fd_dbef$nbsp )

datatable(Functional_dbef, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

MULTIFUNCIONALIDADE

# Combinar os Dados com a Base
dados_combinados <- base %>%
  left_join(cobertura_media, by = c("plot", "treatment", "year")) %>%
  left_join(biomass_summary, by = c("plot", "treatment", "year")) %>%
  left_join(atv_micro, by = c("plot", "treatment", "year")) %>%
  left_join(herbivoria_total, by = c("plot", "treatment", "year")) %>%
  left_join(predacao, by = c("plot", "treatment", "year"))

dbef_multifunc <- cbind(dados_combinados, getStdAndMeanFunctions(dados_combinados, func_vars))

DATASET COMPLETAO

dataset_final_JENA <- dbef_multifunc %>%
  left_join(Functional_dbef, by = c("plot", "treatment", "year"))

datatable(dataset_final_JENA, options= list(deferRender = TRUE,  scrollY = "350px", scrollX=T,   scroller = TRUE,  autoWidth=T), rownames=T)

Análise

cor_matrix

##           raoq       FRic       FEve      FDis        FD
## raoq 1.0000000 0.55127018 0.33838727 0.9545245 0.4651809
## FRic 0.5512702 1.00000000 0.09063385 0.4986894 0.6718329
## FEve 0.3383873 0.09063385 1.00000000 0.3517603 0.2450418
## FDis 0.9545245 0.49868937 0.35176030 1.0000000 0.4626148
## FD   0.4651809 0.67183287 0.24504183 0.4626148 1.0000000

ggcorrplot(cor_matrix, 
           method = "circle", 
           type = "lower", 
           lab = TRUE, 
           title = "Índices de Diversidade Funcional")

Mixed model

mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|year),data = jena)
summary(mod1)

##  Family: gaussian  ( identity )
## Formula:          meanFunction ~ raoq + FRic + (1 | year)
## Data: jena
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1285.8  -1266.2    647.9  -1295.8      364 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance  Std.Dev.
##  year     (Intercept) 0.0008237 0.02870 
##  Residual             0.0016904 0.04111 
## Number of obs: 369, groups:  year, 3
## 
## Dispersion estimate for gaussian family (sigma^2): 0.00169 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  2.125e-01  1.723e-02  12.334  < 2e-16 ***
## raoq        -2.653e-04  1.732e-04  -1.532 0.125645    
## FRic         1.402e-04  4.223e-05   3.319 0.000903 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod_resid <- simulateResiduals(fittedModel = mod1, plot = FALSE)
plot(mod_resid) # resíduos não tá normal

outliers(mod_resid)

## [1] 363

ggplot(jena, aes(x = raoq, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 164 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 164 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(jena, aes(x = raoq, y = meanFunction, color = as.factor(year))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 164 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 164 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(jena, aes(x = raoq, y = meanFunction, color = as.factor(year))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 164 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 164 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(jena, aes(x = FRic, y = meanFunction, color = as.factor(year))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
  labs(title = "FRic",
       x = "FRic", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 351 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 351 rows containing missing values or values outside the scale range
## (`geom_point()`).

#Tirando outliers
jena_semot <- jena %>% 
  filter(!raoq >75)

mod1 <- glmmTMB(meanFunction ~ raoq + FRic + (1|year),data = jena_semot)
summary(mod1)

##  Family: gaussian  ( identity )
## Formula:          meanFunction ~ raoq + FRic + (1 | year)
## Data: jena_semot
## 
##      AIC      BIC   logLik deviance df.resid 
##  -1281.6  -1262.0    645.8  -1291.6      363 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance  Std.Dev.
##  year     (Intercept) 0.0008164 0.02857 
##  Residual             0.0016935 0.04115 
## Number of obs: 368, groups:  year, 3
## 
## Dispersion estimate for gaussian family (sigma^2): 0.00169 
## 
## Conditional model:
##               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  2.118e-01  1.720e-02  12.311  < 2e-16 ***
## raoq        -2.293e-04  1.840e-04  -1.247  0.21247    
## FRic         1.375e-04  4.253e-05   3.232  0.00123 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ggplot(jena_semot, aes(x = raoq, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 160 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 160 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(jena_semot, aes(x = raoq, y = meanFunction, color = as.factor(year))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
  labs(title = "RAOQ",
       x = "raoq", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 160 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 160 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(jena_semot, aes(x = FRic, y = meanFunction)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "FRic",
       x = "FRic", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 347 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 347 rows containing missing values or values outside the scale range
## (`geom_point()`).

ggplot(jena_semot, aes(x = FRic, y = meanFunction, color=as.factor(year))) +
  geom_point() +
  geom_smooth(aes(group = as.factor(year)), method = "lm", se = FALSE) +
  labs(title = "FRic",
       x = "FRic", y = "meanFunction") +
  theme_get()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 347 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 347 rows containing missing values or values outside the scale range
## (`geom_point()`).

BIODEPTH

Gabi

2024-10-15

Biodepth

Dados

Biomassa

FUNCIONAL

MULTIFUNCIONALIDADE

Dataset final

Análise

Mixed model

Ceedar Creek

e097

Dados

Biomassa

MULTIFUNCIONALIDADE

Dataset final

Análise

Mixed model

e120

COMPOSIÇÃO DAS ESPECIES

FUNCIONAL

MULTIFUNCIONALIDADE

Dataset COMPLETO

Análise

Mixed model

Jena dBEF

COMPOSIÇÃO DAS ESPÉCIES: cobertura

FUNCIONAL

MULTIFUNCIONALIDADE

DATASET COMPLETAO

Análise

Mixed model